Exploiting Social Media Features for Automated Tag Recommendation

In today’s social media platforms, when users upload or share their media (photos, videos, bookmarks, etc.), they often annotate it with keywords (called tags). Annotating the media helps in retrieving and browsing resources, and also allows the users to search and browse annotated media. In many social media platforms like Flickr or YouTube, users have to manually annotate their resources, which is inconvenient and time consuming. Tag recommendation is the process of suggesting relevant tags for a given resource, and a tag recommender is a system that recommends the tags. A tag recommender system is important for social media platforms to help users in annotating their resources. Many of the existing tag recommendation methods exploit only the tagging information (Jaschke et al., 2007, Marinho & Schmidt-Thieme, 2008, Sigurbjornsson & van Zwol, 2008). However, many social media platforms support other media features like geographical coordinates. These features can be exploited for improving tag recommendation. In this chapter, a comparison of three types of social media features for tag recommendation is presented and evaluated. The features presented in this chapter include geographical-coordinates, low-level image descriptors, and tags.

Enhancing Social Networks with Agent and Semantic Web Technologies

In this chapter, the authors describe the relationships between multi-agent systems, social networks, and the Semantic Web within collaborative work; they also review how the integration of multi-agent systems and Semantic Web technologies and techniques can be used to enhance social networks at all scales. The chapter first provides a review of relevant work on the application of agent-based models and abstractions to the key ingredients of our work: collaborative systems, the Semantic Web, and social networks. Then, the chapter discusses the reasons current multi-agent systems and their foreseen evolution might be a fundamental means for the realization of the future Semantic Social Networks. Finally, some conclusions are drawn.

Knowledge Management

Organisational culture plays an important role for enabling the process of knowledge sharing. Organisational culture is not only reflected in the visible aspects of organization such as structure, mission, and objectives, it is also embedded in the behaviour of people. The purpose of this chapter is to close research gaps present in knowledge sharing success by examining the linkages between employees’ knowledge-sharing through collaboration, perceived values of involvement, trustworthiness, and formal recognition. The research data was collected by using simple random sampling techniques from a population of knowledge workers in Malaysian IT organisations. The findings highlight the importance of organisational culture for successful knowledge sharing within organisations. The results of factor analysis show the emergence of four new cultural values extant in the Malaysian context. These values are involvement, trustworthiness, formal recognition, and independence. Successful knowledge sharing is significantly related to the perceived value of involvement, trustworthiness, and formal recognition. This chapter will be beneficial for researchers, practitioners, scholars, and organisations (leaders and employees); it will also be helpful for those interested in organisational structure and relationships across organisations in knowledge contexts.

Developing a Web-Based Cooperative Environment to Software Project Development

A software project is developed by collaboration of some expert people. However, the collaboration puts obstacles in the way of software development when the involved people in the project are scattered over the world. Although Internet has provided a collection of scattered islands in which the denizens of the islands are able to communicate with each other, it lacks full requisite qualifications for the collaboration among the denizens. The emerging idea is that a supportive environment should be developed on the Web for providing full requisite qualifications and facilitating collaboration. Towards providing such an environment, this chapter aims to present a framework exploiting Open Hypermedia System (OHS) and a Web-based collaboration protocol. OHS assists in saving and restoring artifacts constructed by the scattered people, and the protocol provides channels to concurrent communication and distributed authoring among the people.

Techniques for Named Entity Recognition

While building and using a fully semantic understanding of Web contents is a distant goal, named entities (NEs) provide a small, tractable set of elements carrying a well-defined semantics. Generic named entities are names of persons, locations, organizations, phone numbers, and dates, while domain-specific named entities includes names of for example, proteins, enzymes, organisms, genes, cells, et cetera, in the biological domain. An ability to automatically perform named entity recognition (NER) – i.e., identify occurrences of NE in Web contents – can have multiple benefits, such as improving the expressiveness of queries and also improving the quality of the search results. A number of factors make building highly accurate NER a challenging task. Given the importance of NER in semantic processing of text, this chapter presents a detailed survey of NER techniques for English text.

The Reflexive Practitioner

This chapter aims to set out relevant discourse and approaches to consider when planning strategies for acquiring and building knowledge for formal ontology construction. Action Research (AR) is offered as a key means to help structure the necessary reflexivity required to enrich the researcher’s understanding of how they know what they know, particularly within a collaborative research setting. This is especially necessary when revealing tacit domain knowledge through participation with actors and stakeholders: “In this kind of research it is permissible to be openly normative and to strive for change, but not to neglect critical reflection” (Elfors & Svane 2008, 1).

Collaborative Mediation

Individuals are the generators and consumers of content, and in doing so, make up a substantial presence in the literate internet, above and beyond the formal media outlets that make up the minority. Accelerating the explosion of content are Web 2.0 interactions, where participants are encouraged to engage with primary content. These social spaces are a platform, supporting often-overlooked micro-interactions referred to in this chapter as digital fingerprints. In parallel, companies construct web experiences that uniquely deliver Internet inspired experiences. However, the competition that divides popular Internet destinations is absent in well run intranets. Collaboration and cooperation among internal web properties offer a unique opportunity to organize people and information across disparate experiences. An example of such a solution is IBM’s Enterprise Tagging System, a collaborative classification and recommendation service that knits employee identities and destinations together through fingerprints. The benefit of creating such a common service also exhibits the side effect and power of the relative few participants. It introduces the desperate need to consider how actions and relationships affect user experiences. The success of social systems requires a high level of diverse participation. This diversity is what ensures the mediation and influence of co-creation and collaborative filtering is not overly narrow.

Knowledge Worker Performance in a Cross-Industrial Perspective

Knowledge workers in specific professional domains form the fastest increasing workforce in OECD countries. Since this fact has been realised by management researchers, they have focussed on the question of how to measure and enhance the productivity of said workforce. According to the author’s cross-industrial research undertaken in five different knowledge-intensive organisations, it is, however, not productivity in the traditional meaning of the term which is to be regarded as the crucial performance indicator in knowledge work. There rather exist multiple performance indicators, each of which is, moreover, differently graded as to its importance by different stakeholders. These findings, firstly, indicate the need for an alternative definition of productivity when the term is applied to knowledge work. Secondly, they indicate the need for alternative definitions of the specific challenges that might be involved in making knowledge workers productive. Thirdly, they imply different consequences for the management of knowledge workers. This chapter closes abovementioned research gaps by summarising the indicators employed in five knowledge-intensive organisations from different business sectors for the assessment of knowledge workers’ performance, by subsequently deducing the specific challenges involved in the management of knowledge workers and by further delineating consequences for the management of knowledge workers – consequences affecting various knowledge-intensive industries.

Ontological Collaboration Engineering

This chapter gives a comprehensive overview of ongoing research about semantic approaches for Collaboration Engineering. The authors present a new ontology-based approach, where each concept of the ontology corresponds to a specific collaboration step or a resource, to collect, manage, and share collaborative knowledge. The chapter discusses the utility of the proposed ontology in the context of a real-world example where the authors explain how collaboration can be modelled and applied using their ontology in order to improve the collaboration process. Furthermore, they discuss how well-known ontologies, such as FOAF, can be linked to their ontology and extend it. While the focus of the chapter is on semantic Collaboration Engineering, the authors additionally present methods of reasoning and machine learning to derive new knowledge about the collaboration process as a further research direction.

Data Mining, Validation, and Collaborative Knowledge Capture

For large-scale data mining, utilizing data from ubiquitous and mixed-structured data sources, the extraction and integration into a comprehensive data-warehouse is usually of prime importance. Then, appropriate methods for validation and potential refinement are essential. This chapter describes an approach for integrating data mining, information extraction, and validation with collaborative knowledge management and capture in order to improve the data acquisition processes. For collaboration, a semantic wiki-enabled system for knowledge and experience management is presented. The proposed approach applies information extraction techniques together with pattern mining methods for initial data validation and is applicable for heterogeneous sources, i.e., capable of integrating structured and unstructured data. The methods are integrated into an incremental process providing for continuous validation options. The approach has been developed in a health informatics context: The results of a medical application demonstrate that pattern mining and the applied rule-based information extraction methods are well suited for discovering, extracting and validating clinically relevant knowledge, as well as the applicability of the knowledge capture approach. The chapter presents experiences using a case-study in the medical domain of sonography.